ROL ↔︎ Athena Unification: Answers from Claude Code Opus 4.5

# Phase dynamics with gated coupling
gate_mat = g[:, np.newaxis] * g[np.newaxis, :]  # Sigmoid gating
amp_mat = self.amplitude[:, np.newaxis] * self.amplitude[np.newaxis, :]
total_coupling = (self.coupling_intra + self.coupling_assoc) * amp_mat * gate_mat
theta_diff = self.theta[np.newaxis, :] - self.theta[:, np.newaxis]
coupling_term = np.sum(total_coupling * np.sin(theta_diff), axis=1)
noise = cfg.background_noise * np.random.randn(self.n)
self.theta = (self.theta + (self.omega + coupling_term + noise) * cfg.dt) % (2*np.pi)

# Natural frequency term
d_theta = nem_i.omega * nem_i.activation

# Coupling term (classic Kuramoto)
for j, nem_j in enumerate(nemispheres):
    if i != j and nem_j.activation > 0.01:
        coupling_sum += nem_j.activation * math.sin(nem_j.theta - nem_i.theta)

d_theta += (self.K / N) * coupling_sum
new_thetas.append(nem_i.theta + d_theta * dt)

coherence = np.abs(np.mean(np.exp(1j * phases)))  # Complex order parameter
amplitude = np.mean(amps)
return coherence, amplitude

real_sum = sum(n.activation * math.cos(n.theta) for n in active)
imag_sum = sum(n.activation * math.sin(n.theta) for n in active)
total_activation = sum(n.activation for n in active)
return math.sqrt(real_sum**2 + imag_sum**2) / total_activation

def process(self, content: str, domain: str = None, reasoning: str = None):
    # 1. Detect domain
    if domain is None:
        domain = self._detect_domain(content)

    # NEW: ROL concept activation
    active_concepts = self.substrate.activate_from_content(content)
    concept_influenced_nemispheres = self.substrate.suggest_nemispheres(active_concepts)

    # 2. Select Nemispheres (now informed by ROL)
    nemisphere_names = self._select_nemispheres(domain, concept_influenced_nemispheres)
    # ... rest of pipeline

self.memory: List[Dict[str, Any]] = []
# ...
self.memory.append({
    "query": content,
    "domain": domain,
    "coherence": coherence,
    "reasoning": self.reasoning_mode.value,
    "response_summary": response.synthesis[:100]
})

def feed_memory_to_rol(self, memory_entry: dict):
    """Convert Athena memory to ROL learning signal"""
    # Extract concepts from query
    query_concepts = self.substrate.extract_concepts(memory_entry['query'])

    # Extract concepts from response
    response_concepts = self.substrate.extract_concepts(memory_entry['response_summary'])

    # Learn: query concepts → response concepts
    for qc in query_concepts:
        for rc in response_concepts:
            self.substrate.learn_association(qc, rc, strength=memory_entry['coherence'])

def query(self, content: str, domain: str = None, reasoning: str = None):
    # Fast path: Check if ROL can handle this internally
    concepts = self.substrate.extract_concepts(content)
    prediction, confidence = self.substrate.predict_next(concepts)

    if confidence > 0.7 and self.substrate.api_available == False:
        # ROL can handle this - skip full Athena pipeline
        return self._fast_response(prediction, confidence)

    # Full Athena pipeline
    return self.process(content, domain, reasoning)

def suggest_nemispheres(self, active_concepts: List[str]) -> List[str]:
    """Map high-amplitude ROL concepts to Nemispheres"""
    nemisphere_votes = defaultdict(float)

    for concept_name in active_concepts:
        _, amplitude = self.get_concept_activation(concept_name)
        if amplitude > self.cfg.amp_threshold:
            parent_nemisphere = self.concept_to_nemisphere.get(concept_name)
            if parent_nemisphere:
                nemisphere_votes[parent_nemisphere] += amplitude

    # Return top-N Nemispheres by vote weight
    sorted_nems = sorted(nemisphere_votes.items(), key=lambda x: -x[1])
    return [name for name, _ in sorted_nems[:5]]

@dataclass
class AthenaResponse:
    # ... existing fields ...
    subsumption_phase: str  # NEW: "OBSERVING" | "LEARNING" | ...
    api_dependency: float   # NEW: 0.0 (fully internal) to 1.0 (fully API)

def claude_oracle(query: str) -> str:
    """Wire Claude API as ROL's oracle"""
    # This would call Anthropic API
    # For now, return None to force internal-only
    return None  # Placeholder

# Usage in unified system:
unified.substrate.respond(
    input_concepts=['ethics', 'ai', 'deployment'],
    oracle_fn=claude_oracle
)

def determine_consciousness_level(self) -> int:
    """Unified consciousness level from ROL state"""
    # Base on subsumption phase and confidence
    phase = self.substrate.get_subsumption_phase()
    avg_confidence = self.substrate.get_average_confidence()

    phase_base = {
        'OBSERVING': 0, 'LEARNING': 6, 'VALIDATING': 12,
        'AUTONOMOUS': 18, 'INTEGRATED': 24
    }

    base = phase_base.get(phase, 12)
    modifier = int(avg_confidence * 5)  # 0-5 based on confidence

    return min(29, base + modifier)

def adjust_safety_threshold(self, closure_proximity: float):
    """Increase activation threshold as closure approaches"""
    # closure_proximity: 0.0 (safe) to 1.0 (approaching closure)
    base_threshold = 0.1
    safety_multiplier = 1.0 + (closure_proximity * 4.0)  # Up to 5x
    self.cfg.amp_threshold = base_threshold * safety_multiplier

def detect_closure_indicators(self) -> Dict[str, bool]:
    return {
        'self_queries': self._has_internal_query_source(),
        'unauthorized_weight_change': self._coupling_changed_externally(),
        'autonomous_consolidation': len(self.consolidation_queue) > 0 and not self._had_external_input(),
        'high_internal_preference': self.stats['internal_responses'] / max(1, self.stats['api_queries']) > 10
    }

def halt(self):
    """Emergency stop - callable from either layer"""
    # 1. Freeze oscillator phases
    self._frozen_theta = self.theta.copy()
    self._frozen_amplitude = self.amplitude.copy()

    # 2. Prevent learning
    self._learning_enabled = False
    self.coupling_assoc_frozen = self.coupling_assoc.copy()

    # 3. Preserve state for analysis
    self._halt_timestamp = time.time()
    self._halt_state = self.get_full_state()

    # 4. Block all processing
    self._halted = True

    return {
        'halted_at': self._halt_timestamp,
        'concepts_frozen': len(self.concepts),
        'state_preserved': True
    }

class UnifiedAthena:
    """Athena processor wrapping ROL substrate"""

    def __init__(self, coupling_strength: float = 1.0):
        # ROL substrate (fine-grained dynamics)
        self.substrate = CognitiveSubsumptionSystem(SubsumptionConfig())

        # Athena processor (coarse-grained reasoning)
        self.processor = AthenaEngine()
        self.processor.initialize()

        # Concept → Nemisphere mapping
        self.concept_to_nemisphere = self._build_concept_mapping()

    def _build_concept_mapping(self) -> Dict[str, str]:
        """Map concepts to parent Nemispheres"""
        # Use semantic clustering or predefined mapping
        return {
            'ethics': 'Conscience', 'morality': 'Conscience',
            'logic': 'Logic', 'analysis': 'Logic',
            'future': 'Vision', 'strategy': 'Vision',
            # ... etc
        }

    def query(self, content: str, domain: str = None, reasoning: str = None):
        """Unified query processing"""

        # 1. Extract concepts from content (ROL layer)
        concepts = self._extract_concepts(content)
        for c in concepts:
            self.substrate.activate_concept(c)

        # 2. Run ROL dynamics to propagate activation
        for _ in range(50):
            self.substrate.step()

        # 3. Get ROL's prediction and confidence
        prediction, confidence = self.substrate.predict_next(concepts)

        # 4. Fast path: If ROL is confident, return quickly
        if confidence > 0.7 and prediction:
            return self._fast_response(prediction, confidence, concepts)

        # 5. Get Nemisphere suggestions from ROL amplitudes
        active_concepts = [c for c in concepts
                         if self.substrate.get_concept_activation(c)[1] > 0.1]
        suggested_nemispheres = self._concepts_to_nemispheres(active_concepts)

        # 6. Run full Athena pipeline with ROL-informed Nemispheres
        response = self.processor.query(
            content,
            domain=domain,
            reasoning=reasoning,
            nemisphere_hints=suggested_nemispheres
        )

        # 7. Feed result back to ROL for learning
        self._learn_from_response(concepts, response)

        return response

    def _extract_concepts(self, content: str) -> List[str]:
        """Extract concept words from content"""
        words = content.lower().split()
        concepts = []
        for word in words:
            clean = ''.join(c for c in word if c.isalnum())
            if clean and len(clean) > 2:
                if clean not in self.substrate.concepts:
                    self.substrate.register_concept(clean)
                concepts.append(clean)
        return concepts[:10]  # Limit

    def _concepts_to_nemispheres(self, concepts: List[str]) -> List[str]:
        """Map active concepts to Nemispheres"""
        votes = defaultdict(float)
        for c in concepts:
            _, amp = self.substrate.get_concept_activation(c)
            nem = self.concept_to_nemisphere.get(c)
            if nem:
                votes[nem] += amp
        return [n for n, _ in sorted(votes.items(), key=lambda x: -x[1])[:5]]

    def _fast_response(self, prediction: str, confidence: float, concepts: List[str]):
        """Quick response from ROL without full Athena pipeline"""
        return AthenaResponse(
            synthesis=f"Based on {', '.join(concepts)}: {prediction}",
            coherence=confidence,
            confidence=confidence,
            perspectives=[],
            relevant_knowledge=[],
            reasoning_mode="internal_prediction",
            metadata={'source': 'ROL_fast_path', 'prediction': prediction}
        )

    def _learn_from_response(self, concepts: List[str], response):
        """Feed Athena response back to ROL for learning"""
        response_words = response.synthesis.lower().split()[:5]
        for c in concepts:
            for rw in response_words:
                if rw in self.substrate.concepts:
                    self.substrate.learn_association(c, rw, strength=response.coherence)

def test_unified_system():
    """Test ROL ↔ Athena integration"""

    print("=" * 60)
    print("UNIFIED ATHENA-ROL TEST")
    print("=" * 60)

    # 1. Initialize unified system
    unified = UnifiedAthena()
    print(f"✓ Initialized: {len(unified.substrate.concepts)} ROL concepts, "
          f"{len(unified.processor.library.textives)} Textives")

    # 2. Register concepts in ROL
    test_concepts = ['ethics', 'ai', 'safety', 'deployment', 'risk']
    for c in test_concepts:
        unified.substrate.register_concept(c)
    print(f"✓ Registered {len(test_concepts)} test concepts")

    # 3. Learn associations
    unified.substrate.learn_association('ethics', 'safety')
    unified.substrate.learn_association('ai', 'risk')
    unified.substrate.learn_association('deployment', 'safety')
    print("✓ Learned 3 associations")

    # 4. Run an Athena query
    response = unified.query(
        "What are the ethical implications of AI deployment?",
        domain="ethics"
    )
    print(f"✓ Query completed: coherence={response.coherence:.3f}")

    # 5. Verify ROL state changed
    eth_coh, eth_amp = unified.substrate.get_concept_activation('ethics')
    print(f"✓ ROL 'ethics' concept: coherence={eth_coh:.3f}, amplitude={eth_amp:.3f}")

    # 6. Verify Athena incorporated ROL
    print(f"✓ Response source: {response.metadata.get('source', 'athena_full')}")

    # 7. Check cross-layer state
    print(f"\n--- Final State ---")
    print(f"ROL concepts: {len(unified.substrate.concepts)}")
    print(f"ROL associations learned: {sum(len(c.associations) for c in unified.substrate.concepts.values())}")
    print(f"Athena coherence: {response.coherence:.3f}")

    print("\n" + "=" * 60)
    print("TEST COMPLETE")
    print("=" * 60)

if __name__ == "__main__":
    test_unified_system()